Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Friday 19 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 150,688 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 150,678 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 5
## 108 2020-06-16 East of England 2
## 109 2020-06-17 East of England 3
## 110 2020-03-01 London 0
## 111 2020-03-02 London 0
## 112 2020-03-03 London 0
## 113 2020-03-04 London 0
## 114 2020-03-05 London 0
## 115 2020-03-06 London 1
## 116 2020-03-07 London 1
## 117 2020-03-08 London 0
## 118 2020-03-09 London 1
## 119 2020-03-10 London 0
## 120 2020-03-11 London 6
## 121 2020-03-12 London 6
## 122 2020-03-13 London 10
## 123 2020-03-14 London 14
## 124 2020-03-15 London 10
## 125 2020-03-16 London 15
## 126 2020-03-17 London 22
## 127 2020-03-18 London 27
## 128 2020-03-19 London 25
## 129 2020-03-20 London 44
## 130 2020-03-21 London 49
## 131 2020-03-22 London 54
## 132 2020-03-23 London 63
## 133 2020-03-24 London 87
## 134 2020-03-25 London 113
## 135 2020-03-26 London 129
## 136 2020-03-27 London 130
## 137 2020-03-28 London 122
## 138 2020-03-29 London 146
## 139 2020-03-30 London 149
## 140 2020-03-31 London 181
## 141 2020-04-01 London 202
## 142 2020-04-02 London 190
## 143 2020-04-03 London 196
## 144 2020-04-04 London 230
## 145 2020-04-05 London 195
## 146 2020-04-06 London 197
## 147 2020-04-07 London 220
## 148 2020-04-08 London 238
## 149 2020-04-09 London 206
## 150 2020-04-10 London 170
## 151 2020-04-11 London 177
## 152 2020-04-12 London 158
## 153 2020-04-13 London 166
## 154 2020-04-14 London 144
## 155 2020-04-15 London 142
## 156 2020-04-16 London 139
## 157 2020-04-17 London 100
## 158 2020-04-18 London 101
## 159 2020-04-19 London 103
## 160 2020-04-20 London 95
## 161 2020-04-21 London 94
## 162 2020-04-22 London 109
## 163 2020-04-23 London 77
## 164 2020-04-24 London 71
## 165 2020-04-25 London 58
## 166 2020-04-26 London 53
## 167 2020-04-27 London 51
## 168 2020-04-28 London 43
## 169 2020-04-29 London 44
## 170 2020-04-30 London 40
## 171 2020-05-01 London 41
## 172 2020-05-02 London 40
## 173 2020-05-03 London 36
## 174 2020-05-04 London 30
## 175 2020-05-05 London 25
## 176 2020-05-06 London 37
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## 178 2020-05-08 London 30
## 179 2020-05-09 London 23
## 180 2020-05-10 London 26
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## 183 2020-05-13 London 16
## 184 2020-05-14 London 20
## 185 2020-05-15 London 18
## 186 2020-05-16 London 14
## 187 2020-05-17 London 15
## 188 2020-05-18 London 9
## 189 2020-05-19 London 14
## 190 2020-05-20 London 19
## 191 2020-05-21 London 12
## 192 2020-05-22 London 10
## 193 2020-05-23 London 6
## 194 2020-05-24 London 7
## 195 2020-05-25 London 9
## 196 2020-05-26 London 12
## 197 2020-05-27 London 7
## 198 2020-05-28 London 8
## 199 2020-05-29 London 7
## 200 2020-05-30 London 12
## 201 2020-05-31 London 6
## 202 2020-06-01 London 10
## 203 2020-06-02 London 7
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## 205 2020-06-04 London 8
## 206 2020-06-05 London 4
## 207 2020-06-06 London 0
## 208 2020-06-07 London 4
## 209 2020-06-08 London 5
## 210 2020-06-09 London 2
## 211 2020-06-10 London 7
## 212 2020-06-11 London 5
## 213 2020-06-12 London 3
## 214 2020-06-13 London 3
## 215 2020-06-14 London 2
## 216 2020-06-15 London 1
## 217 2020-06-16 London 1
## 218 2020-06-17 London 0
## 219 2020-03-01 Midlands 0
## 220 2020-03-02 Midlands 0
## 221 2020-03-03 Midlands 1
## 222 2020-03-04 Midlands 0
## 223 2020-03-05 Midlands 0
## 224 2020-03-06 Midlands 0
## 225 2020-03-07 Midlands 0
## 226 2020-03-08 Midlands 3
## 227 2020-03-09 Midlands 1
## 228 2020-03-10 Midlands 0
## 229 2020-03-11 Midlands 2
## 230 2020-03-12 Midlands 6
## 231 2020-03-13 Midlands 5
## 232 2020-03-14 Midlands 4
## 233 2020-03-15 Midlands 5
## 234 2020-03-16 Midlands 11
## 235 2020-03-17 Midlands 8
## 236 2020-03-18 Midlands 13
## 237 2020-03-19 Midlands 8
## 238 2020-03-20 Midlands 28
## 239 2020-03-21 Midlands 13
## 240 2020-03-22 Midlands 31
## 241 2020-03-23 Midlands 33
## 242 2020-03-24 Midlands 41
## 243 2020-03-25 Midlands 48
## 244 2020-03-26 Midlands 64
## 245 2020-03-27 Midlands 72
## 246 2020-03-28 Midlands 89
## 247 2020-03-29 Midlands 92
## 248 2020-03-30 Midlands 90
## 249 2020-03-31 Midlands 123
## 250 2020-04-01 Midlands 140
## 251 2020-04-02 Midlands 142
## 252 2020-04-03 Midlands 124
## 253 2020-04-04 Midlands 151
## 254 2020-04-05 Midlands 164
## 255 2020-04-06 Midlands 140
## 256 2020-04-07 Midlands 123
## 257 2020-04-08 Midlands 186
## 258 2020-04-09 Midlands 139
## 259 2020-04-10 Midlands 127
## 260 2020-04-11 Midlands 142
## 261 2020-04-12 Midlands 139
## 262 2020-04-13 Midlands 120
## 263 2020-04-14 Midlands 116
## 264 2020-04-15 Midlands 147
## 265 2020-04-16 Midlands 102
## 266 2020-04-17 Midlands 118
## 267 2020-04-18 Midlands 115
## 268 2020-04-19 Midlands 92
## 269 2020-04-20 Midlands 107
## 270 2020-04-21 Midlands 86
## 271 2020-04-22 Midlands 78
## 272 2020-04-23 Midlands 103
## 273 2020-04-24 Midlands 79
## 274 2020-04-25 Midlands 72
## 275 2020-04-26 Midlands 81
## 276 2020-04-27 Midlands 74
## 277 2020-04-28 Midlands 68
## 278 2020-04-29 Midlands 53
## 279 2020-04-30 Midlands 56
## 280 2020-05-01 Midlands 64
## 281 2020-05-02 Midlands 51
## 282 2020-05-03 Midlands 52
## 283 2020-05-04 Midlands 61
## 284 2020-05-05 Midlands 58
## 285 2020-05-06 Midlands 59
## 286 2020-05-07 Midlands 48
## 287 2020-05-08 Midlands 34
## 288 2020-05-09 Midlands 37
## 289 2020-05-10 Midlands 42
## 290 2020-05-11 Midlands 33
## 291 2020-05-12 Midlands 45
## 292 2020-05-13 Midlands 40
## 293 2020-05-14 Midlands 37
## 294 2020-05-15 Midlands 40
## 295 2020-05-16 Midlands 34
## 296 2020-05-17 Midlands 31
## 297 2020-05-18 Midlands 34
## 298 2020-05-19 Midlands 34
## 299 2020-05-20 Midlands 36
## 300 2020-05-21 Midlands 32
## 301 2020-05-22 Midlands 27
## 302 2020-05-23 Midlands 34
## 303 2020-05-24 Midlands 19
## 304 2020-05-25 Midlands 26
## 305 2020-05-26 Midlands 33
## 306 2020-05-27 Midlands 29
## 307 2020-05-28 Midlands 27
## 308 2020-05-29 Midlands 20
## 309 2020-05-30 Midlands 20
## 310 2020-05-31 Midlands 22
## 311 2020-06-01 Midlands 20
## 312 2020-06-02 Midlands 22
## 313 2020-06-03 Midlands 24
## 314 2020-06-04 Midlands 15
## 315 2020-06-05 Midlands 21
## 316 2020-06-06 Midlands 20
## 317 2020-06-07 Midlands 16
## 318 2020-06-08 Midlands 15
## 319 2020-06-09 Midlands 17
## 320 2020-06-10 Midlands 14
## 321 2020-06-11 Midlands 13
## 322 2020-06-12 Midlands 12
## 323 2020-06-13 Midlands 6
## 324 2020-06-14 Midlands 16
## 325 2020-06-15 Midlands 11
## 326 2020-06-16 Midlands 8
## 327 2020-06-17 Midlands 1
## 328 2020-03-01 North East and Yorkshire 0
## 329 2020-03-02 North East and Yorkshire 0
## 330 2020-03-03 North East and Yorkshire 0
## 331 2020-03-04 North East and Yorkshire 0
## 332 2020-03-05 North East and Yorkshire 0
## 333 2020-03-06 North East and Yorkshire 0
## 334 2020-03-07 North East and Yorkshire 0
## 335 2020-03-08 North East and Yorkshire 0
## 336 2020-03-09 North East and Yorkshire 0
## 337 2020-03-10 North East and Yorkshire 0
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## 339 2020-03-12 North East and Yorkshire 0
## 340 2020-03-13 North East and Yorkshire 0
## 341 2020-03-14 North East and Yorkshire 0
## 342 2020-03-15 North East and Yorkshire 2
## 343 2020-03-16 North East and Yorkshire 3
## 344 2020-03-17 North East and Yorkshire 1
## 345 2020-03-18 North East and Yorkshire 2
## 346 2020-03-19 North East and Yorkshire 6
## 347 2020-03-20 North East and Yorkshire 5
## 348 2020-03-21 North East and Yorkshire 6
## 349 2020-03-22 North East and Yorkshire 7
## 350 2020-03-23 North East and Yorkshire 9
## 351 2020-03-24 North East and Yorkshire 8
## 352 2020-03-25 North East and Yorkshire 18
## 353 2020-03-26 North East and Yorkshire 21
## 354 2020-03-27 North East and Yorkshire 28
## 355 2020-03-28 North East and Yorkshire 35
## 356 2020-03-29 North East and Yorkshire 38
## 357 2020-03-30 North East and Yorkshire 64
## 358 2020-03-31 North East and Yorkshire 60
## 359 2020-04-01 North East and Yorkshire 67
## 360 2020-04-02 North East and Yorkshire 74
## 361 2020-04-03 North East and Yorkshire 100
## 362 2020-04-04 North East and Yorkshire 105
## 363 2020-04-05 North East and Yorkshire 92
## 364 2020-04-06 North East and Yorkshire 96
## 365 2020-04-07 North East and Yorkshire 102
## 366 2020-04-08 North East and Yorkshire 107
## 367 2020-04-09 North East and Yorkshire 111
## 368 2020-04-10 North East and Yorkshire 117
## 369 2020-04-11 North East and Yorkshire 98
## 370 2020-04-12 North East and Yorkshire 84
## 371 2020-04-13 North East and Yorkshire 94
## 372 2020-04-14 North East and Yorkshire 107
## 373 2020-04-15 North East and Yorkshire 96
## 374 2020-04-16 North East and Yorkshire 103
## 375 2020-04-17 North East and Yorkshire 88
## 376 2020-04-18 North East and Yorkshire 95
## 377 2020-04-19 North East and Yorkshire 88
## 378 2020-04-20 North East and Yorkshire 100
## 379 2020-04-21 North East and Yorkshire 76
## 380 2020-04-22 North East and Yorkshire 84
## 381 2020-04-23 North East and Yorkshire 63
## 382 2020-04-24 North East and Yorkshire 72
## 383 2020-04-25 North East and Yorkshire 69
## 384 2020-04-26 North East and Yorkshire 65
## 385 2020-04-27 North East and Yorkshire 65
## 386 2020-04-28 North East and Yorkshire 57
## 387 2020-04-29 North East and Yorkshire 69
## 388 2020-04-30 North East and Yorkshire 57
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## 390 2020-05-02 North East and Yorkshire 48
## 391 2020-05-03 North East and Yorkshire 40
## 392 2020-05-04 North East and Yorkshire 49
## 393 2020-05-05 North East and Yorkshire 40
## 394 2020-05-06 North East and Yorkshire 51
## 395 2020-05-07 North East and Yorkshire 45
## 396 2020-05-08 North East and Yorkshire 42
## 397 2020-05-09 North East and Yorkshire 44
## 398 2020-05-10 North East and Yorkshire 40
## 399 2020-05-11 North East and Yorkshire 29
## 400 2020-05-12 North East and Yorkshire 27
## 401 2020-05-13 North East and Yorkshire 28
## 402 2020-05-14 North East and Yorkshire 30
## 403 2020-05-15 North East and Yorkshire 32
## 404 2020-05-16 North East and Yorkshire 35
## 405 2020-05-17 North East and Yorkshire 26
## 406 2020-05-18 North East and Yorkshire 29
## 407 2020-05-19 North East and Yorkshire 27
## 408 2020-05-20 North East and Yorkshire 21
## 409 2020-05-21 North East and Yorkshire 33
## 410 2020-05-22 North East and Yorkshire 22
## 411 2020-05-23 North East and Yorkshire 18
## 412 2020-05-24 North East and Yorkshire 25
## 413 2020-05-25 North East and Yorkshire 21
## 414 2020-05-26 North East and Yorkshire 21
## 415 2020-05-27 North East and Yorkshire 22
## 416 2020-05-28 North East and Yorkshire 20
## 417 2020-05-29 North East and Yorkshire 25
## 418 2020-05-30 North East and Yorkshire 20
## 419 2020-05-31 North East and Yorkshire 20
## 420 2020-06-01 North East and Yorkshire 16
## 421 2020-06-02 North East and Yorkshire 22
## 422 2020-06-03 North East and Yorkshire 22
## 423 2020-06-04 North East and Yorkshire 17
## 424 2020-06-05 North East and Yorkshire 17
## 425 2020-06-06 North East and Yorkshire 21
## 426 2020-06-07 North East and Yorkshire 13
## 427 2020-06-08 North East and Yorkshire 11
## 428 2020-06-09 North East and Yorkshire 11
## 429 2020-06-10 North East and Yorkshire 16
## 430 2020-06-11 North East and Yorkshire 6
## 431 2020-06-12 North East and Yorkshire 8
## 432 2020-06-13 North East and Yorkshire 10
## 433 2020-06-14 North East and Yorkshire 11
## 434 2020-06-15 North East and Yorkshire 8
## 435 2020-06-16 North East and Yorkshire 9
## 436 2020-06-17 North East and Yorkshire 3
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## 449 2020-03-13 North West 3
## 450 2020-03-14 North West 1
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## 452 2020-03-16 North West 2
## 453 2020-03-17 North West 4
## 454 2020-03-18 North West 6
## 455 2020-03-19 North West 7
## 456 2020-03-20 North West 10
## 457 2020-03-21 North West 11
## 458 2020-03-22 North West 13
## 459 2020-03-23 North West 15
## 460 2020-03-24 North West 21
## 461 2020-03-25 North West 21
## 462 2020-03-26 North West 29
## 463 2020-03-27 North West 35
## 464 2020-03-28 North West 28
## 465 2020-03-29 North West 46
## 466 2020-03-30 North West 67
## 467 2020-03-31 North West 52
## 468 2020-04-01 North West 86
## 469 2020-04-02 North West 96
## 470 2020-04-03 North West 95
## 471 2020-04-04 North West 98
## 472 2020-04-05 North West 102
## 473 2020-04-06 North West 100
## 474 2020-04-07 North West 135
## 475 2020-04-08 North West 127
## 476 2020-04-09 North West 119
## 477 2020-04-10 North West 117
## 478 2020-04-11 North West 138
## 479 2020-04-12 North West 125
## 480 2020-04-13 North West 129
## 481 2020-04-14 North West 131
## 482 2020-04-15 North West 114
## 483 2020-04-16 North West 135
## 484 2020-04-17 North West 98
## 485 2020-04-18 North West 113
## 486 2020-04-19 North West 71
## 487 2020-04-20 North West 83
## 488 2020-04-21 North West 76
## 489 2020-04-22 North West 86
## 490 2020-04-23 North West 85
## 491 2020-04-24 North West 66
## 492 2020-04-25 North West 65
## 493 2020-04-26 North West 55
## 494 2020-04-27 North West 54
## 495 2020-04-28 North West 57
## 496 2020-04-29 North West 62
## 497 2020-04-30 North West 59
## 498 2020-05-01 North West 45
## 499 2020-05-02 North West 56
## 500 2020-05-03 North West 55
## 501 2020-05-04 North West 48
## 502 2020-05-05 North West 48
## 503 2020-05-06 North West 44
## 504 2020-05-07 North West 49
## 505 2020-05-08 North West 42
## 506 2020-05-09 North West 30
## 507 2020-05-10 North West 41
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## 509 2020-05-12 North West 38
## 510 2020-05-13 North West 25
## 511 2020-05-14 North West 26
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## 513 2020-05-16 North West 32
## 514 2020-05-17 North West 24
## 515 2020-05-18 North West 31
## 516 2020-05-19 North West 35
## 517 2020-05-20 North West 27
## 518 2020-05-21 North West 26
## 519 2020-05-22 North West 26
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## 521 2020-05-24 North West 26
## 522 2020-05-25 North West 31
## 523 2020-05-26 North West 27
## 524 2020-05-27 North West 27
## 525 2020-05-28 North West 28
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## 527 2020-05-30 North West 17
## 528 2020-05-31 North West 13
## 529 2020-06-01 North West 12
## 530 2020-06-02 North West 27
## 531 2020-06-03 North West 21
## 532 2020-06-04 North West 20
## 533 2020-06-05 North West 15
## 534 2020-06-06 North West 23
## 535 2020-06-07 North West 18
## 536 2020-06-08 North West 19
## 537 2020-06-09 North West 15
## 538 2020-06-10 North West 13
## 539 2020-06-11 North West 15
## 540 2020-06-12 North West 6
## 541 2020-06-13 North West 7
## 542 2020-06-14 North West 11
## 543 2020-06-15 North West 14
## 544 2020-06-16 North West 8
## 545 2020-06-17 North West 4
## 546 2020-03-01 South East 0
## 547 2020-03-02 South East 0
## 548 2020-03-03 South East 1
## 549 2020-03-04 South East 0
## 550 2020-03-05 South East 1
## 551 2020-03-06 South East 0
## 552 2020-03-07 South East 0
## 553 2020-03-08 South East 1
## 554 2020-03-09 South East 1
## 555 2020-03-10 South East 1
## 556 2020-03-11 South East 1
## 557 2020-03-12 South East 0
## 558 2020-03-13 South East 1
## 559 2020-03-14 South East 1
## 560 2020-03-15 South East 5
## 561 2020-03-16 South East 8
## 562 2020-03-17 South East 7
## 563 2020-03-18 South East 10
## 564 2020-03-19 South East 9
## 565 2020-03-20 South East 13
## 566 2020-03-21 South East 7
## 567 2020-03-22 South East 25
## 568 2020-03-23 South East 20
## 569 2020-03-24 South East 22
## 570 2020-03-25 South East 29
## 571 2020-03-26 South East 35
## 572 2020-03-27 South East 34
## 573 2020-03-28 South East 36
## 574 2020-03-29 South East 55
## 575 2020-03-30 South East 58
## 576 2020-03-31 South East 65
## 577 2020-04-01 South East 66
## 578 2020-04-02 South East 55
## 579 2020-04-03 South East 72
## 580 2020-04-04 South East 80
## 581 2020-04-05 South East 82
## 582 2020-04-06 South East 88
## 583 2020-04-07 South East 100
## 584 2020-04-08 South East 83
## 585 2020-04-09 South East 104
## 586 2020-04-10 South East 88
## 587 2020-04-11 South East 88
## 588 2020-04-12 South East 88
## 589 2020-04-13 South East 84
## 590 2020-04-14 South East 65
## 591 2020-04-15 South East 72
## 592 2020-04-16 South East 56
## 593 2020-04-17 South East 86
## 594 2020-04-18 South East 57
## 595 2020-04-19 South East 70
## 596 2020-04-20 South East 87
## 597 2020-04-21 South East 50
## 598 2020-04-22 South East 54
## 599 2020-04-23 South East 57
## 600 2020-04-24 South East 64
## 601 2020-04-25 South East 51
## 602 2020-04-26 South East 51
## 603 2020-04-27 South East 40
## 604 2020-04-28 South East 40
## 605 2020-04-29 South East 47
## 606 2020-04-30 South East 29
## 607 2020-05-01 South East 37
## 608 2020-05-02 South East 36
## 609 2020-05-03 South East 17
## 610 2020-05-04 South East 35
## 611 2020-05-05 South East 29
## 612 2020-05-06 South East 25
## 613 2020-05-07 South East 27
## 614 2020-05-08 South East 26
## 615 2020-05-09 South East 28
## 616 2020-05-10 South East 19
## 617 2020-05-11 South East 25
## 618 2020-05-12 South East 27
## 619 2020-05-13 South East 18
## 620 2020-05-14 South East 32
## 621 2020-05-15 South East 24
## 622 2020-05-16 South East 22
## 623 2020-05-17 South East 18
## 624 2020-05-18 South East 22
## 625 2020-05-19 South East 12
## 626 2020-05-20 South East 22
## 627 2020-05-21 South East 14
## 628 2020-05-22 South East 17
## 629 2020-05-23 South East 21
## 630 2020-05-24 South East 17
## 631 2020-05-25 South East 13
## 632 2020-05-26 South East 19
## 633 2020-05-27 South East 18
## 634 2020-05-28 South East 12
## 635 2020-05-29 South East 21
## 636 2020-05-30 South East 8
## 637 2020-05-31 South East 10
## 638 2020-06-01 South East 11
## 639 2020-06-02 South East 13
## 640 2020-06-03 South East 17
## 641 2020-06-04 South East 11
## 642 2020-06-05 South East 11
## 643 2020-06-06 South East 10
## 644 2020-06-07 South East 11
## 645 2020-06-08 South East 7
## 646 2020-06-09 South East 9
## 647 2020-06-10 South East 10
## 648 2020-06-11 South East 5
## 649 2020-06-12 South East 5
## 650 2020-06-13 South East 4
## 651 2020-06-14 South East 6
## 652 2020-06-15 South East 6
## 653 2020-06-16 South East 7
## 654 2020-06-17 South East 2
## 655 2020-03-01 South West 0
## 656 2020-03-02 South West 0
## 657 2020-03-03 South West 0
## 658 2020-03-04 South West 0
## 659 2020-03-05 South West 0
## 660 2020-03-06 South West 0
## 661 2020-03-07 South West 0
## 662 2020-03-08 South West 0
## 663 2020-03-09 South West 0
## 664 2020-03-10 South West 0
## 665 2020-03-11 South West 1
## 666 2020-03-12 South West 0
## 667 2020-03-13 South West 0
## 668 2020-03-14 South West 1
## 669 2020-03-15 South West 0
## 670 2020-03-16 South West 0
## 671 2020-03-17 South West 2
## 672 2020-03-18 South West 2
## 673 2020-03-19 South West 4
## 674 2020-03-20 South West 3
## 675 2020-03-21 South West 6
## 676 2020-03-22 South West 7
## 677 2020-03-23 South West 8
## 678 2020-03-24 South West 7
## 679 2020-03-25 South West 9
## 680 2020-03-26 South West 11
## 681 2020-03-27 South West 13
## 682 2020-03-28 South West 21
## 683 2020-03-29 South West 18
## 684 2020-03-30 South West 23
## 685 2020-03-31 South West 23
## 686 2020-04-01 South West 22
## 687 2020-04-02 South West 23
## 688 2020-04-03 South West 30
## 689 2020-04-04 South West 42
## 690 2020-04-05 South West 32
## 691 2020-04-06 South West 34
## 692 2020-04-07 South West 39
## 693 2020-04-08 South West 47
## 694 2020-04-09 South West 24
## 695 2020-04-10 South West 46
## 696 2020-04-11 South West 43
## 697 2020-04-12 South West 23
## 698 2020-04-13 South West 27
## 699 2020-04-14 South West 24
## 700 2020-04-15 South West 32
## 701 2020-04-16 South West 29
## 702 2020-04-17 South West 33
## 703 2020-04-18 South West 25
## 704 2020-04-19 South West 31
## 705 2020-04-20 South West 26
## 706 2020-04-21 South West 26
## 707 2020-04-22 South West 23
## 708 2020-04-23 South West 17
## 709 2020-04-24 South West 19
## 710 2020-04-25 South West 15
## 711 2020-04-26 South West 27
## 712 2020-04-27 South West 13
## 713 2020-04-28 South West 17
## 714 2020-04-29 South West 15
## 715 2020-04-30 South West 26
## 716 2020-05-01 South West 6
## 717 2020-05-02 South West 7
## 718 2020-05-03 South West 10
## 719 2020-05-04 South West 17
## 720 2020-05-05 South West 14
## 721 2020-05-06 South West 19
## 722 2020-05-07 South West 16
## 723 2020-05-08 South West 6
## 724 2020-05-09 South West 11
## 725 2020-05-10 South West 5
## 726 2020-05-11 South West 8
## 727 2020-05-12 South West 7
## 728 2020-05-13 South West 7
## 729 2020-05-14 South West 6
## 730 2020-05-15 South West 4
## 731 2020-05-16 South West 4
## 732 2020-05-17 South West 6
## 733 2020-05-18 South West 4
## 734 2020-05-19 South West 6
## 735 2020-05-20 South West 1
## 736 2020-05-21 South West 9
## 737 2020-05-22 South West 6
## 738 2020-05-23 South West 6
## 739 2020-05-24 South West 3
## 740 2020-05-25 South West 8
## 741 2020-05-26 South West 11
## 742 2020-05-27 South West 5
## 743 2020-05-28 South West 10
## 744 2020-05-29 South West 7
## 745 2020-05-30 South West 3
## 746 2020-05-31 South West 2
## 747 2020-06-01 South West 7
## 748 2020-06-02 South West 2
## 749 2020-06-03 South West 5
## 750 2020-06-04 South West 2
## 751 2020-06-05 South West 2
## 752 2020-06-06 South West 1
## 753 2020-06-07 South West 3
## 754 2020-06-08 South West 3
## 755 2020-06-09 South West 0
## 756 2020-06-10 South West 0
## 757 2020-06-11 South West 2
## 758 2020-06-12 South West 2
## 759 2020-06-13 South West 2
## 760 2020-06-14 South West 0
## 761 2020-06-15 South West 0
## 762 2020-06-16 South West 0
## 763 2020-06-17 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 18 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -9.215 -2.760 -0.260 2.834 4.805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.965e+00 5.240e-02 94.75 <2e-16 ***
## note_lag 1.147e-05 5.217e-07 21.99 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.59199)
##
## Null deviance: 5488.03 on 47 degrees of freedom
## Residual deviance: 501.66 on 46 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 143.356267 1.000011
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 129.21939 158.690218
## note_lag 1.00001 1.000012
Rsq(lag_mod)
## [1] 0.9085905
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0